# 1. Introduction — macop v1.0.5 documentation

# 1. Introduction

Macop is a python package for solving optimisation problems that are both discrete and continuous in nature. The objective is to allow a user to exploit the basic structure proposed by this package to solve a problem specific to him. The interest is that he can quickly abstract himself from the complications related to the way of evaluating, comparing, saving the progress of the search for good solutions but rather concentrate if necessary on his own algorithm. Indeed, Macop offers the following main and basic features:

- solutions: representation of the solution ;

- evaluator: stores problem instance data and implement a compute method in order to evaluate a solution ;

- operators: mutators, crossovers update of solution ;

- policies: the way you choose the available operators (might be using reinforcement learning) ;

- callbacks: callbacks to automatically keep track of the search space advancement.

- algorithms: generic and implemented optimisation research algorithms ;

Based on all of these generic and/or implemented functionalities, the user will be able to quickly develop a solution to his problem while retaining the possibility of remaining in control of his development by overloading existing functionalities if necessary.